Personalized search is an important means to improve the retrieval effectiveness of a search engine, since user queries are normally short and ambiguous. Thus, it is hard for a search engine to figure out what the users precisely want. Most commercial search engines simply return the same set of results to all users who ask the same query. However, different users may have different preferences on the result set. Thus, personalization is needed in order to rank the results according to a user's personal preferences. In this thesis, we develop two methods to mine a user's conceptual preferences from search engine clickthrough data, and adjust the search result ranking according to the extracted preferences to improve the retrieval effectiveness for the user....[ Read more ]

Personalized search is an important means to improve the retrieval effectiveness of a search engine, since user queries are normally short and ambiguous. Thus, it is hard for a search engine to figure out what the users precisely want. Most commercial search engines simply return the same set of results to all users who ask the same query. However, different users may have different preferences on the result set. Thus, personalization is needed in order to rank the results according to a user's personal preferences. In this thesis, we develop two methods to mine a user's conceptual preferences from search engine clickthrough data, and adjust the search result ranking according to the extracted preferences to improve the retrieval effectiveness for the user.

We first propose a framework that supports mining a user's conceptual preferences from users' clickthrough data resulting from web search. The discovered preferences are utilized to adapt a search engine's ranking function. In the framework, an extended set of conceptual preferences is derived for a user based on the concepts extracted from the search results and the clickthrough data. Then, an Ontology-based User Profile (OUP) representing the user profile as a concept ontology tree is generated. Finally, the OUP is input to a Support Vector Machine (SVM) to learn a concept preference vector for adapting a personalized ranking function that re-ranks the search results. We confirm that our approach is able to improve significantly the retrieval effectiveness for the user.

We then adopt the OUP approach in the area of location-based personalization. We propose a personalized mobile search engine, PMSE, that captures the users' preferences in the form of concepts by mining their clickthrough data. Due to the importance of location information in mobile search, PMSE classifies these concepts into content concepts and location concepts. In addition, users' locations (positioned by GPS) are used to supplement the location concepts in PMSE. The user preferences are organized in an ontology-based, multi-facet user profile, which are used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the diversity of the concepts associated with a query and their relevance to the user's need, four entropies are introduced to balance the weights between the content and location facets. We also present a detailed client-server architecture of PMSE. In our design, the client collects and stores locally the clickthrough data to protect privacy, whereas resource consuming tasks such as concept extraction, training and reranking are performed at the PMSE server. We prototype PMSE on the Google Android platform. Experimental results show that PMSE significantly improves retrieval effectiveness compared to the baseline without personalization.